Optimized User Experience for Labeling Systems for Predictive Maintenance Applications (Extended)

Abstract

The maintenance of rail vehicles and infrastructure plays a critical role in reducing delays, preventing malfunctions, and ensuring the economic efficiency of rail transportation companies. Predictive maintenance systems powered by supervised machine learning offer a promising approach by detecting failures before they occur, reducing unscheduled downtime, and improving operational efficiency. However, the success of such systems depends on high quality labeled data, necessitating user centered labeling interfaces tailored to annotators needs for Usability and User Experience. This study introduces a cost effective predictive maintenance system developed in the federally funded project DigiOnTrack, which combines structure borne noise measurement with supervised learning to provide monitoring and maintenance recommendations for rail vehicles and infrastructure in rural Germany. The system integrates wireless sensor networks, distributed ledger technology for secure data transfer, and a dockerized container infrastructure hosting the labeling interface and dashboard. Train drivers and workshop foremen labeled faults on infrastructure and vehicles to ensure accurate recommendations. The Usability and User Experience evaluation showed that the locomotive drivers interface achieved Excellent Usability, while the workshop foremans interface was rated as Good. These results highlight the systems potential for integration into daily workflows, particularly in labeling efficiency. However, areas such as Perspicuity require further optimization for more data intensive scenarios. The findings offer insights into the design of predictive maintenance systems and labeling interfaces, providing a foundation for future guidelines in Industry 4.0 applications, particularly in rail transportation.